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Fig 1.

Block diagram of the proposed human anxiety classification scheme in response to exposure therapy.

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Fig 2.

Timing diagram of the data acquisition protocol followed in DASPS database.

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Fig 3.

Raw EEG data obtained from the 14 channels of the Emotiv EPOC headset.

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Fig 4.

Percentage of instances using SAM score based labeling for (a) Two-level (b) Four-level and HAM-A based lableling for (c) Two-level (d) Four-level anxiety classification.

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Fig 5.

Box-and-whisker diagram of the power obtained from each channel of the Emotiv EPOC headset for binary anxiety classification.

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Fig 6.

Box-and-whisker diagram of the power obtained from each channel of the Emotiv EPOC headset for four level anxiety classification.

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Fig 7.

Classification accuracy of DT, k-NN, SVM, MLP and RF classifier using features from different frequency band combinations of EEG signal using SAM score based labeling for (a) Two-levels (b) Four-levels anxiety classification.

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Fig 8.

Classification accuracy of DT, k-NN, SVM, MLP and RF classifier using features from different frequency band combinations of EEG signal using HAM-A based labeling for (a) Two-levels (b) Four-levels anxiety classification.

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Table 1.

Performance comparison of proposed anxiety classification framework using both SAM and HAM-A based labeling for two and four levels in terms of different machine learning classifiers, Feature Vector Length (FVL), accuracy, F-value, and Kappa values (K).

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Table 2.

Confusion matrix for two level anxiety classification for DT, kNN, SVM, MLP and RF classifier using SAM based labeling.

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Table 3.

Confusion matrix for two level anxiety classification for DT, kNN, SVM, MLP and RF classifier using HAM-A based labeling.

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Table 3 Expand

Table 4.

Confusion matrix for four level anxiety classification for DT, k-NN, SVM, MLP and RF classifier using SAM based labeling.

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Table 4 Expand

Table 5.

Confusion matrix for four level anxiety classification for DT, k-NN, SVM, MLP and RF classifier using HAM-A based labeling.

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Table 6.

Performance comparison of the proposed framework with the state-of-the-art methods for human state anxiety classification.

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